class_docstr: Augmenter that leverage semantic meaning to substitute word.
  
  :param str lang: Language of your text. Default value is 'eng'.
  :param float aug_p: Percentage of word will be augmented.
  :param int aug_min: Minimum number of word will be augmented.
  :param int aug_max: Maximum number of word will be augmented. If None is passed, number of augmentation is
      calculated via aup_p. If calculated result from aug_p is smaller than aug_max, will use calculated result from
      aug_p. Otherwise, using aug_max.
  :param list stopwords: List of words which will be skipped from augment operation.
  :param str stopwords_regex: Regular expression for matching words which will be skipped from augment operation.
  :param func tokenizer: Customize tokenization process
  :param func reverse_tokenizer: Customize reverse of tokenization process
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.word as naw
  >>> aug = naw.AntonymAug()
code: def _get_aug_idxes(self, tokens):
      aug_cnt = self.generate_aug_cnt(len(tokens))
      word_idxes = self.pre_skip_aug(tokens, tuple_idx=0)
      word_idxes = self.skip_aug(word_idxes, tokens)
      if len(word_idxes) == 0:
          if self.verbose > 0:
              exception = WarningException(
                  name=WarningName.OUT_OF_VOCABULARY,
                  code=WarningCode.WARNING_CODE_002,
                  msg=WarningMessage.NO_WORD,
              )
              exception.output()
          return None
  
      aug_idexes = []
      for aug_idx in word_idxes:
          word_poses = PartOfSpeech.constituent2pos(tokens[aug_idx][1])
          candidates = []
          if word_poses is None or len(word_poses) == 0:
              # Use every possible words as the mapping does not defined correctly
              candidates.extend(self.model.predict(tokens[aug_idx][0]))
          else:
              for word_pos in word_poses:
                  candidates.extend(
                      self.model.predict(
                          tokens[aug_idx][0], pos=word_pos
                      )
                  )
  
          candidates = [
              c
              for c in candidates
              if c.lower() != tokens[aug_idx][0].lower()
          ]
  
          if len(candidates) > 0:
              candidate = self.sample(candidates, 1)[0]
              aug_idexes.append((aug_idx, candidate))
  
      if len(aug_idexes) < aug_cnt:
          aug_cnt = len(aug_idexes)
  
      aug_idexes = self.sample(aug_idexes, aug_cnt)
      return aug_idexes
  
file_path: nlpaug/nlpaug/augmenter/word/antonym.py
imports: from nlpaug.augmenter.word import WordAugmenter
  from nlpaug.util import Action, Doc, PartOfSpeech, WarningException, WarningName, WarningCode, WarningMessage
  import nlpaug.model.word_dict
label_desc: Use image node_id 3 for calling the AntonymAug obj's underlying member method code with example usage: obj._get_aug_idxes(tokens) and returns: aug_idexes, None